In an iris recognition system, there is a need for correct extraction of an iris region or user's region-of-interest (ROI) from a given iris image to enhance performance and efficiency. Further, if geometrical features such as shape, position, rotated angle (i.e. an angle of an eye moved from the horizontal), etc. of an iris region can be determined within the eye image, these features can also be used as very important information in iris recognition. Further, information about the degree of covering the iris, the kind of object covering the iris, and photographed conditions of an iris image may be very useful in an iris recognition system.
Generally, the aforementioned information regarding an eye image can be regarded as region-of-interest information of a user and cognitive information recognized by a person. Although such information can be automatically obtained by certain algorithms, it is very difficult to correctly obtain this information or to obtain an eye image in a desired direction. For example, in the case of obtaining iris boundary information, it is difficult to set up the boundary of the iris region, if the boundary of the iris region is blurred by change in illumination around a camera when photographing the iris image or if the iris region and its boundary are covered by an eyebrow, an eyelid, glasses, etc.
Work such as determination of an inner region and a boundary of an object in a digital image refers to image segmentation, which is one of the most difficult operations in digital image processing. The image segmentation refers to segmenting a given image into several regions or objects, the main purpose of which is to distinguish and completely isolate a region-of-interest from other objects in the given image. Various image segmentation techniques are used in the art. As general automatic segmentation methods, a segmentation method based upon detection of a sudden change within an image, a segmentation method based upon gradual enlargement of a region having a similar feature, a segmentation method based upon modelling of an object to be detected, and the like are used in the art.
However, most conventional segmentation methods pertain to a bottom-up type which attempts to directly determine a desired image from an individual image. In this case, however, an undesired segmentation result can be obtained under conditions that an eye image includes serious noise, that an iris region has a non-finite boundary due to covering of eyelashes, etc. Further, even though the boundary is correctly determined from such a non-finite form, it is different to determine the region and the boundary in a desired form (e.g., in a definite form easy for a user to manipulate), and thus, it is difficult to obtain a satisfactory result using existing automatic techniques.
To solve this problem, a semiautomatic segmentation technique, in which a user makes a rough segmentation guideline and applies a certain algorithm to the rest of segmentation, is used. This method has a disadvantage in that a user must make guidelines one by one with regard to all images.
Even in the case of semantic information or circumstantial information about an eye image as well as information about the aforementioned geometrical region, a person can easily recognize such information from a digital image, but it is very difficult to automatically extract the information through an algorithm. Over the past few decades, various bottom-up techniques have been proposed in the field of vision, machine learning, and artificial intelligence, but these techniques are not useful in systematic and correct extraction of the semantic information or circumstantial information.
In recent Webs, various attempts have been made to enable intelligent Web searching based on the semantic and circumstantial information under the title of semantic Web. However, these attempts are simply for current search-oriented applications, not for extracting such information from given media.